Causal inference in data science

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The document discusses using correlations to build predictive models and compares the success rates of two algorithms, Algorithm A and Algorithm B, on different user groups. It also notes that while average comment length decreases over time across all users, length increases over time for each yearly cohort. References are provided on causation and spurious correlations.

Data & Analytics

1
http://www.amitsharma.in
http://www.github.com/amit-sharma/causal-inference-
tutorial

Use these correlations to make a predictive model.
Future Activity ->
f(number of friends, logins in past month)

6

19
Algorithm A Algorithm B
50/1000 (5%) 54/1000 (5.4%)

20
Algorithm A Algorithm B
10/400 (2.5%) 4/200 (2%)
Algorithm A Algorithm B
40/600 (6.6%) 50/800 (6.2%)

Is Algorithm A better?
Algorithm A Algorithm B
Success Rate for
Low-Activity users
10/400 (2.5%) 4/200 (2%)
Success Rate for
High-Activity users
40/600 (6.6%) 50/800 (6.2%)
Total Success Rate 50/1000 (5%) 54/1000 (5.4%)
21

Average comment length decreases over time.
23
But for each yearly cohort of users, comment length
increases over time.

27http://plato.stanford.edu/entries/causation-mani/

28http://plato.stanford.edu/entries/causation-counterfactual/

38Dunning (2002), Rosenzweig-Wolpin (2000)

47http://tylervigen.com/spurious-correlations

http://www.github.com/amit-sharma/causal-inference-tutorial
amshar@microsoft.com
48

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Can we predict the future popularity of a song, movie or tweet? Recent work suggests that although it may be hard to predict an item’s popularity when it is first introduced, peeking into its early adopters and properties of their social network makes the problem easier. We test the robustness of such claims by using data from social networks spanning music, books, photos, and URLs. We find a stronger result: not only do predictive models with peeking achieve high accuracy on all datasets, they also generalize well, so much so that models trained on any one dataset perform with comparable accuracy on items from other datasets. Though practically useful, our models (and those in other work) are intellectually unsatisfying because common formulations of the problem, which involve peeking at the first small-k adopters and predicting whether items end up in the top half of popular items, are both too sensitive to the speed of early adoption and too easy. Most of the predictive power comes from looking at how quickly items reach their first few adopters, while for other features of early adopters and their networks, even the direction of correlation with popularity is not consistent across domains. Problem formulations that examine items that reach k adopters in about the same amount of time reduce the importance of temporal features, but also overall accuracy, highlighting that we understand little about why items become popular while providing a context in which we might build that understanding.

Predictability of popularity on online social media: Gaps between prediction ...

Amit Sharma

How much do activity feeds in online social networks influence our actions? Short of doing a randomized experiment, this is a very tricky question to answer. This is because it requires answering the counterfactual: "how would users' behavior have changed if there was no feed in a social networking website?" We propose a data-driven matching procedure that is able to control for underlying homophily effects and estimate the effect of feeds on actions in multiple social networks, including Last.fm, Goodreads, Flickr and Flixster. We show that most actions in these social networks are not influenced by the feed at all, suggesting that influence from friends' feeds may be overrated.

Estimating influence of online activity feeds on people's actions

Amit Sharma

Graphical models present a useful abstraction of causal pathways, but there are numerous causal pathways in the real world. It is often not clear whether a causal graph correctly models a given scenario, or when it does, whether we have enough data to estimate causal effects. In such a messy scenario, it would not be a bad decision to abandon the search for causality altogether, if not for the far-reaching, important effects of causality in almost all branches of scientific inquiry. Here are some examples: • Economic Policy: How much does the school you go to affect your future earnings? How bad is military transcription for future earnings or wellbeing of an individual? • Political science: How do televised ads from presidential candidates affect voter turnouts? Does mandating quotas for women in local government lead to changes in policy? • Human behavior: Does public recognition of scholastic ability lead to better performance? If you friend smokes, would you smoke too? • Biology and medicine: Does Vitamin-D deficiency increase chances of colorectal cancer? Are heart attack surgeries beneficial for future health of patients? • Web applications: How much does the Facebook newsfeed influence the information we are exposed to? How much do recommendation systems shape our purchasing decisions? In this talk, we will select a bunch of these as motivating examples and discuss the four main methodologies for estimating causal effects. All through, our goal will be make connections to bayesian networks/graphical models and show how they can serve as a unifying framework for dealing with causality. Outline: • Controlled randomized experiments Conditioning on observable confounders (back door criterion) -- Stratification -- Matching -- Regression(?) • Mechanism-based strategies (Front door criterion) • Natural experiments -- As-if experiments -- Instrumental variables -- Regression discontinuities

Causal inference in practice: Here, there, causality is everywhere

Amit Sharma

The interplay of personal preference and social influence in sharing networks...

Amit Sharma

The role of social connections in shaping our preferences

Amit Sharma

[RecSys '13]Pairwise Learning: Experiments with Community Recommendation on L...

Amit Sharma

RSWEB 2013: A research platform for social recommendation

Amit Sharma

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Causal inference in data science

1. 1 http://www.amitsharma.in http://www.github.com/amit-sharma/causal-inference- tutorial

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6. Use these correlations to make a predictive model. Future Activity -> f(number of friends, logins in past month)  6

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19. 19 Algorithm A Algorithm B 50/1000 (5%) 54/1000 (5.4%)

20. 20 Algorithm A Algorithm B 10/400 (2.5%) 4/200 (2%) Algorithm A Algorithm B 40/600 (6.6%) 50/800 (6.2%)

21. Is Algorithm A better? Algorithm A Algorithm B Success Rate for Low-Activity users 10/400 (2.5%) 4/200 (2%) Success Rate for High-Activity users 40/600 (6.6%) 50/800 (6.2%) Total Success Rate 50/1000 (5%) 54/1000 (5.4%) 21

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23. Average comment length decreases over time. 23 But for each yearly cohort of users, comment length increases over time.

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27. 27http://plato.stanford.edu/entries/causation-mani/

28. 28http://plato.stanford.edu/entries/causation-counterfactual/

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